Modelling for wrong-way segments

In order to observe the influence of roadway characteristics on the wrong-way riding behavior of cyclists, I used a zero-inflated negative binomial model. Since most of the segments (over 70%) contained zero wrong-way riding, I used the zero-inflated negative binomial model. However, I also tried fitting a simple negative binomial model before fitting a zero-inflated negative binomial model. As a sensitivity test, I tested the models with and without the decay factor calculated as discussed in the previous section. Table 8 shows the AIC and BIC values for each model. Since the models with smaller values are better, ZINB performed better than the simple negative binomial model in both scenarios of using or not using the decay factor.

Table 8 Comparisions of diffrent models used (smaller values better)

Model used AIC BIC

Simple Negative binomial without decay 8307.93 8392.35 with decay 7870.07 7954.49 ZINB without decay 8269.74 8367.15 with decay 7814.46 7924.85

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Table 9 shows the results from the ZINB model with decay factor. Both scenarios (using or not using decay factor) produced similar results, so only the model with decay factor is shown. Predictors for presence of different bike infrastructure, AADT and number of lanes in the segment in the negative binomial regression model predicting wrong-way riding counts were significant. Similarly, predictors for total wrong-way counts, segment length, and AADT in the logit part predicting excessive zeros were statistically

significant.

Table 9 Results from ZINB model with decay factor

_{Coef. Std. Err. z P>|z|} NB state Total counts 0.02 0.00 6.35 0.00 Segment length 0.00 0.00 4.88 0.00 Sharrow -1.07 0.46 -2.34 0.02 Bike lane 0.76 0.17 4.37 0.00

Buffered bike lane -0.94 0.29 -3.24 0.00 Trails/Sidewalk 1.86 0.50 3.71 0.00

Connector -0.29 0.14 -2.17 0.03

No bike lanes 0.00 (omitted)

AADT_high -0.59 0.10 -5.84 0.00

More than 1 lane 0.51 0.12 4.15 0.00

constant -0.99 0.10 -10.13 0.00 Zero State Total counts -0.53 0.13 -4.27 0.00 Segment length -0.02 0.01 -3.34 0.00 No bike lanes 0.09 0.66 0.14 0.89 AADT_high 4.04 1.00 4.05 0.00

More than 1 lane -2.41 2.54 -0.95 0.34

_constant -0.69 1.32 -0.52 0.60

Looking at results from logit part (or zero state), the log odds of observing no wrong-way riding in the segment would decrease by 0.53 for every additional trip taken in the

segment. This result is intuitive as higher number of trips taken over a segment would increase the chances of observing wrong-way over the segment. Similarly, the log odds of observing wrong-way on roads on higher AADT was 4.04 times lower than on roads with lower AADT.

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For the negative binomial portion of the model, different bike infrastructure was

correlated with the wrong-way riding. While sharrow lane markings, buffered bike lanes, and connector roads were negatively associated with wrong-way riding, bike lanes and trails were experience more wrong-way riding than roads with no bike facilities. Segments with sharrow lanes were less likely to be travelled in wrong-way direction (expected log(count) of 1.07 lower) than roads with no bike facilities holding other variables constant. This is similar to previous findings from a study in San Francisco where presence of sharrow markings decreased wrong-way riding by 80%(Gajda, Sallaberry et al. 2004). The roads with higher AADT also seemed to discourage wrong- way riding. Roads with low AADT had 1.8 times more wrong-way riding counts than roads with higher AADT. This is also intuitive as riders might feel unsafe when riding wrong-way on a busier street, hence discouraging the behavior. Trails or cycle tracks also showed more wrong-way riding than road with no bike lanes. Cycle tracks are physically separated from motor traffic and distinct from the sidewalk, with a separated path and the on-street infrastructure like that of a conventional bike lane. Similarly, the number of lanes also showed the positive relationship with wrong direction riding. Roads with more than one segment has an expected count 1.66 times higher than segments with a single lane. A possible explanation for this may be that for wider roads, riders would prefer not to cross to ride on the correct side of the road if they’re already present on the wrong side.

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CHAPTER FIVE

CONCLUSIONS AND RECOMMENDATIONS

The main objective of this study was to demonstrate an application of using a naturalistic data for bicycle safety analysis. This study focuses on highlighting the wrong-way riding behavior of cyclists in Philadelphia using a crowdsourced data gathered from an

application. This study is unique because it is the first study focusing on wrong-way riding behavior of cyclists using a naturalistic dataset and is among the first to explore city-wide aberrant behavior using probe data.

The results from the study will help planners and engineers better plan new bike

infrastructure in cities. Segments with a higher number of bike trips showed more wrong- way riding. This could make the case for contra-flow bike lanes on cities like

Philadelphia with many one-way streets and high bike traffic. Contra-flow bike lanes increase connectivity in the network for cyclists, and could improve safety. The results also show the influence of various bike infrastructure on the wrong-way riding behavior of cyclists which will help the engineers in choosing between various type of bike infrastructure. In addition to this, the data used in this study can be further used to study other route choice behaviors of the cyclists with the traditional route choice modeling.

However, there are some limitations of this study. The main limitation is that the

CyclePhilly dataset is not a random dataset and not representative of cyclist population of the whole city. Hence any findings of this study will only reflect the nature of

CyclePhilly users and their travel behavior. While this dataset is not representative of the entire cyclist population, a dataset like this provides a valuable resource in accessing the roadway infrastructure with the resolution of data it provides. In addition to that, I have complemented those data with various other publicly available dataset of traffic counts, bicycle crashes history and road speed limit. I also accounted for a single rider being overrepresented in a road segment. For each segment, I introduced a decay factor that reduces the influence of a single rider making multiple trips.

When using GPS data for bicycle routing, due to inaccuracies associated with the GPS devices, it is hard to accurately plot the paths taken over the road segment and the side of road where the trips take place. This issue limits accurately finding the wrong direction riding for bi-directional roads. Our study area in Philadelphia is full of densely connected grid network of one way streets. This gave us a unique opportunity to correctly identify the wrong direction riding.

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I present this study as an application of methods that can be used to exploit these types of dataset. Furthermore, open source data from OSM provides segment level detail of road infrastructure and can complement the collected data. In this paper, I highlighted an application of using these data to study the riding behaviors of the cyclists.

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VITA

Nirbesh Dhakal was born in Kathmandu, Nepal. After completing his Bachelors in Civil Engineering from Institute of Engineering, Pulchowk, he came to United States to pursue his higher studies. As a graduate student at University of Tennessee, Nirbesh worked as a graduate teaching assistant in Transportation Engineering lab. He also worked as graduate research assistant in transportation planning, and sustainability projects under the guidance of Dr. Christopher Cherry. After graduation, Nirbesh plans to work in the consulting industry as a Transportation Engineer.